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中国科学：信息科学（英文版）
中国科学：信息科学（英文版）

周光召

月刊

1674-733X

informatics@scichina.org

010-64015683

100717

北京东黄城根北街16号

中国科学：信息科学（英文版）/Journal Science China Information SciencesCSCDCSTPCDEISCI
查看更多>>《中国科学》是中国科学院主办、中国科学杂志社出版的自然科学专业性学术刊物。《中国科学》任务是反映中国自然科学各学科中的最新科研成果，以促进国内外的学术交流。《中国科学》以论文形式报道中国基础研究和应用研究方面具有创造性的、高水平的和有重要意义的科研成果。在国际学术界，《中国科学》作为代表中国最高水平的学术刊物也受到高度重视。国际上最具有权威的检索刊物SCI，多年来一直收录《中国科学》的论文。1999年《中国科学》夺得国家期刊奖的第一名。
正式出版
收录年代

    An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

    Jing LIANGYuyang ZHANGKe CHENBoyang QU...
    1-15页
    查看更多>>摘要：Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset.Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs)have been extensively used to address the feature selection problem due to their strong global search capability.However,most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process.In addressing the aforementioned issue,this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition.The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity.Specifi-cally,an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets.Furthermore,a novel knowledge transfer mechanism that considers evolutionary characteristics is developed,allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods.The ex-perimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.
      原文链接:
    • 万方数据
    • 维普

    Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer

    Peng YANGLaoming ZHANGHaifeng LIUGuiying LI...
    16-36页
    查看更多>>摘要：In recent years,various companies have started to shift their data services from traditional data centers to the cloud.One of the major motivations is to save on operational costs with the aid of cloud elasticity.This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections,without disconnecting them from the server side.This paper considers this need as a bi-objective online load balancing problem.A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity.An evolutionary multi-objective training framework is proposed to optimize the weights of the policy.Not only is the new objective of idleness reduced by over 130％more than traditional industrial solutions,but the original load balancing objective itself is also slightly improved.Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.
      原文链接:
    • 万方数据
    • 维普

    Enhancing SAEAs with unevaluated solutions:a case study of relation model for expensive optimization

    Hao HAOXiaoqun ZHANGAimin ZHOU
    37-54页
    查看更多>>摘要：Surrogate-assisted evolutionary algorithms(SAEAs)hold significant importance in resolving ex-pensive optimization problems.Extensive efforts have been devoted to improving the efficiency of SAEAs through the development of proficient model-assisted solution selection methods,which,however,first re-quires generating high-quality solutions.The fundamental paradigm of evaluating a limited number of so-lutions in each generation within SAEAs reduces the variance of adjacent populations,thereby affecting the quality of offspring solutions.This is a frequently encountered issue yet receives little attention.To address the issue,this paper presents a framework using unevaluated solutions to enhance the efficiency of SAEAs.The surrogate model is employed to identify high-quality solutions for the direct generation of new solutions without evaluation.To ensure dependable selection,we have introduced two tailored relation models to select the optimal solution and the unevaluated population.A comprehensive experimental analysis is performed on two test suites,which showcases the superiority of the relation model over regression and classification models in the solution selection phase.Furthermore,the surrogate-selected unevaluated solutions with high potential have been shown to enhance the efficiency of the algorithm significantly.
      原文链接:
    • 万方数据
    • 维普

    A survey on model-based reinforcement learning

    Fan-Ming LUOTian XUHang LAIXiong-Hui CHEN...
    55-80页
    查看更多>>摘要：Reinforcement learning(RL)interacts with the environment to solve sequential decision-making problems via a trial-and-error approach.Errors are always undesirable in real-world applications,even though RL excels at playing complex video games that permit several trial-and-error attempts.To improve sample efficiency and thus reduce errors,model-based reinforcement learning(MBRL)is believed to be a promising direction,as it constructs environment models in which trial-and-errors can occur without incurring actual costs.In this survey,we investigate MBRL with a particular focus on the recent advancements in deep RL.There is a generalization error between the learned model of a non-tabular environment and the actual environment.Consequently,it is crucial to analyze the disparity between policy training in the environment model and that in the actual environment,guiding algorithm design for improved model learning,model utilization,and policy training.In addition,we discuss the recent developments of model-based techniques in other forms of RL,such as offline RL,goal-conditioned RL,multi-agent RL,and meta-RL.Furthermore,we discuss the applicability and benefits of MBRL for real-world tasks.Finally,this survey concludes with a discussion of the promising future development prospects for MBRL.We believe that MBRL has great unrealized potential and benefits in real-world applications,and we hope this survey will encourage additional research on MBRL.
      原文链接:
    • 万方数据
    • 维普

    A survey on the network models applied in the industrial network optimization

    Chao DONGXiaoxiong XIONGQiulin XUEZhengzhen ZHANG...
    81-99页
    查看更多>>摘要：Network architecture design is critical for optimizing industrial networks.Network architectures can be classified into small-scale networks and large-scale networks based on scale.Graph theory is an efficient mathematical tool for network topology modeling.For small-scale networks,their structure often has regular topology.For large-scale ones,the current body of work mainly focuses on random characteristics of network nodes and edges.Recently,widely used models include random networks,small-world networks,and scale-free networks.In this study,starting from the scale of the network,network modeling methods based on graph theory as well as their industrial applications,are summarized and analyzed.Moreover,a novel network performance metric,called system entropy,is proposed.From the perspective of mathematical properties,an analysis of its non-negativity and concavity is performed.The advantage of system entropy is that it can cover the existing regular networks,random networks,small-world networks,and scale-free networks,and has strong generality.The simulation results reveal that this proposed metric can achieve the comparison of various industrial networks under different models.
      原文链接:
    • 万方数据
    • 维普

    Fault-tolerant identity-based encryption from SM9

    Xiaohong LIUXinyi HUANGZhaohui CHENGWei WU...
    100-113页
    查看更多>>摘要：This paper initiates the formal study of attribute-based encryption within the framework of SM9,the Chinese National Cryptography Standard for Identity-Based Cryptography,by presenting two new fault-tolerant identity-based encryption(FIBE)schemes.Our first scheme uses the same private-key/ciphertext structure as the original SM9 algorithm and operates in a small attribute universe.As a result,it can be effectively and smoothly integrated into the information systems using SM9.In the random oracle model,we prove that our scheme is ciphertext-indistinguishable against fuzzy selective-identity and chosen-plaintext attacks under the(k+3)-DBDHI assumption.Our second design is a large universe FIBE scheme based on SM9 that is ciphertext-indistinguishable against chosen-plaintext attacks in the random oracle model under the(f,g)-GDDHE assumption.Finally,we compare the communication and computing costs of our schemes to those of other classical ones.The comparison shows that our schemes have comparable performance as others.We believe that our findings will accelerate the applications of SM9 in modern information systems such as cloud computing and blockchain.
      原文链接:
    • 万方数据
    • 维普

    A FAS approach for stabilization of generalized chained forms:part 1.Discontinuous control laws

    Guang-Ren DUAN
    114-139页
    查看更多>>摘要：In this paper,a type of general nonholonomic systems is proposed,which contains both the Brockett's two example systems,and their extended n-dimensional chained forms,as special cases.For the stabilization of such systems,a stabilizing controller is proposed based on the fully actuated system(FAS)approach,which is discontinuous at the origin but time-invariant when the open-loop system is time-invariant,and drives the feasible trajectories of the system to the origin exponentially.Furthermore,the proposed FAS approach is also extended to the sub-normal system case and the time-delay system case.
      原文链接:
    • 万方数据
    • 维普

    Prescribed-time stabilization and inverse optimal control of stochastic high-order nonlinear systems

    Ran LIUHui WANGWuquan LI
    140-152页
    查看更多>>摘要：This paper investigates the prescribed-time state-feedback stabilization and prescribed-time in-verse optimality problems for stochastic high-order nonlinear systems.First,a time-varying controller is designed by developing scaled quartic Lyapunov functions,which can guarantee that the system has a unique strong solution almost surely on the prescribed interval for any system initial conditions and that the states and the control converge to the origin in a mean-square form within the prescribed time.Then,the controller is redesigned to address the problem of prescribed-time inverse optimal mean-square stabilization.Finally,a concrete example is provided to confirm the efficiency of the proposed design schemes.
      原文链接:
    • 万方数据
    • 维普

    On the convergence of tracking differentiator with multiple stochastic disturbances

    Zehao WUHuacheng ZHOUBaozhu GUOFeiqi DENG...
    153-165页
    查看更多>>摘要：This paper investigates the convergence,noise-tolerance,and filtering performance of a tracking differentiator in the presence of multiple stochastic disturbances for the first time.We consider a general case wherein the input signal is corrupted by additive colored noise,and the tracking differentiator is disturbed by additive colored noise and white noise.The tracking differentiator is shown to track the input signal and its generalized derivatives in the mean square sense.Further,the almost sure convergence can be achieved when the stochastic noise affecting the input signal is vanishing.Herein,numerical simulations are performed to validate the theoretical results.
      原文链接:
    • 万方数据
    • 维普

    Value iteration algorithm for continuous-time linear quadratic stochastic optimal control problems

    Guangchen WANGHeng ZHANG
    166-176页
    查看更多>>摘要：In this study,we investigate a continuous-time infinite-horizon linear quadratic stochastic optimal control problem with multiplicative noise in control and state variables.Using the techniques of stochastic stability,exact observability,and stochastic approximation,a value iteration algorithm is developed to solve the corresponding generalized algebraic Riccati equation.Unlike the existing policy iteration algorithm,this algorithm does not rely on an initial stabilizing control.Further,this algorithm can also be used to compute policy evaluation steps that arise in the policy iteration algorithm.Herein,a simulation example is provided to validate the obtained results.
      原文链接:
    • 万方数据
    • 维普